Quantitative Assessment of Bolt Looseness in Beam–Column Joints Using SH-Typed Guided Waves and Deep Neural Network
Bolt connections are the primary component of beam–column joints, which frequently become loose during their service life due to environmental factors. Assessing the tightness of bolts is essential for maintaining structural integrity and safety. Although the guided wave method has been proven effec...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6425 |
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| Summary: | Bolt connections are the primary component of beam–column joints, which frequently become loose during their service life due to environmental factors. Assessing the tightness of bolts is essential for maintaining structural integrity and safety. Although the guided wave method has been proven effective for detecting bolt looseness, the severe dispersion properties and complex structure of beam–column joints pose difficulties for the quantitative evaluation of bolt looseness. Therefore, a deep neural network model integrating a convolutional neural network (CNN), long short-term memory (LSTM), and multi-head self-attention mechanism (MHSA) is introduced to identify the degree of looseness in multiple bolts using SH-typed guided waves. The dispersion properties of the I-shaped steel beam were analyzed using the semi-analytical finite element method, and a mode weight coefficient was presented to clarify the mode distribution under different types of external loads. Two pairs of transducers arranged on the same side of the bolt-connected region were utilized to obtain the directly incoming and end-reflected wave packets from four wave propagation paths. The received signals were converted into time–frequency spectra, and the effective components were extracted to form the input pattern for the neural network. Numerical simulations were performed on a beam–column joint with eight bolts, and the number of training samples was increased using data augmentation techniques. The results indicate that the CNN-LSTM-MHSA model can accurately estimate the bolt looseness conditions better than other methods. Noise injection testing was also conducted to investigate the effect of measurement noise. |
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| ISSN: | 2076-3417 |